The Fourth Industrial Revolution is a global development that shows no signs of slowing down. In his book, The Workplace of the Future: The Fourth Industrial Revolution, the Precariat and the Death of Hierarchies, Jon-Arild Johannessen sets a chilling vision of how robots and artificial intelligence will completely disrupt and transform working life. The author contests that once the dust has settled from the Fourth Industrial Revolution, workplaces and professions will be unrecognizable and we will see the rise of a new social class: the precariat. We will live side by side with the 'working poor' - people who have several jobs, but still can't make ends meet. There will be a small salaried elite consisting of innovation and knowledge workers. Slightly further into the future, there will be a major transformation in professional environments. Johannessen also presents a typology for the precariat, the uncertain work that is created and develops a framework for the working poor, as well as for future innovation and knowledge workers, and sets out a new structure for the social hierarchy. A fascinating and thought-provoking insight into the impact of the Fourth Industrial Revolution, The Workplace of the Future will be of interest to professionals and academics alike. The book is particularly suited to academic courses in management, economy, political science and social sciences.

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology. New to the Second Edition New chapter on Bayesian network classifiers New section on object-oriented Bayesian networks New section that addresses foundational problems with causal discovery and Markov blanket discovery New section that covers methods of evaluating causal discovery programs Discussions of many common modeling errors New applications and case studies More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks Illustrated with real case studies, the second edition of this bestseller continues to cover the groundwork of Bayesian networks. It presents the elements of Bayesian network technology, automated causal discovery, and learning probabilities from data and shows how to employ these technologies to develop probabilistic expert systems. Web Resource The book's website at www.csse.monash.edu.au/bai/book/book.html offers a variety of supplemental materials, including example Bayesian networks and data sets. Instructors can email the authors for sample solutions to many of the problems in the text.

Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn't learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book shows you how to combine the practicality of early AI with deep learning capabilities and industrial control technologies to make robust decisions in the real world. Using concrete examples, minimal theory, and a proven architectural framework, author Kence Anderson demonstrates how to teach autonomous AI explicit skills and strategies. You'll learn when and how to use and combine various AI architecture design patterns, as well as how to design advanced AI without needing to manipulate neural networks or machine learning algorithms. Students, process operators, data scientists, machine learning algorithm experts, and engineers who own and manage industrial processes can use the methodology in this book to design autonomous AI. This book examines: Differences between and limitations of automated, autonomous, and human decision-making Unique advantages of autonomous AI for real-time decision-making, with use cases How to design an autonomous AI from modular components and document your designs

"Automated scoring engines [...] require a careful balancing of the contributions of technology, NLP, psychometrics, artificial intelligence, and the learning sciences. The present handbook is evidence that the theories, methodologies, and underlying technology that surround automated scoring have reached maturity, and that there is a growing acceptance of these technologies among experts and the public." From the Foreword by Alina von Davier, ACTNext Senior Vice President Handbook of Automated Scoring: Theory into Practice provides a scientifically grounded overview of the key research efforts required to move automated scoring systems into operational practice. It examines the field of automated scoring from the viewpoint of related scientific fields serving as its foundation, the latest developments of computational methodologies utilized in automated scoring, and several large-scale real-world applications of automated scoring for complex learning and assessment systems. The book is organized into three parts that cover (1) theoretical foundations, (2) operational methodologies, and (3) practical illustrations, each with a commentary. In addition, the handbook includes an introduction and synthesis chapter as well as a cross-chapter glossary.

Written by world-class leaders in type-2 fuzzy logic control, this book offers a self-contained reference for both researchers and students. The coverage provides both background and an extensive literature survey on fuzzy logic and related type-2 fuzzy control. It also includes research questions, experiment and simulation results, and downloadable computer programs on an associated website. This key resource will prove useful to students and engineers wanting to learn type-2 fuzzy control theory and its applications.

The healthcare industry is on the cutting edge of voice-user interface (VUI) design and making great progress to improve patient care through developing technologies, literally transforming the voice of the industry. The advantages of VUI extend far beyond simple conveniences for patients or a healthcare employee's saved phone call. VUI has a profound impact on care improvement. Just like a person, a well-designed VUI can use tone of voice, inflection and other elements in conversation to shape behaviors or calm nerves. With VUI, physicians and patients become empowered to make informed decisions about healthcare. The use of voice technology across smart speakers, IoT, clinical and home devices, and wearables for improving the patient experience and clinical outcomes was recently identified as one of most significant emerging technologies in healthcare. Smart speakers are the #1 selling consumer item in the world and major competition is heating up between Amazon Alexa, Apple Siri, Microsoft Cortana, Google Voice Assistant, and a host of other specialty platforms specific to healthcare. From Orbita and Macadamia to voice-enabled robotics from Pillo, Intuitive, Vivify, and RealView Imaging, voice technology is pervasive across the gamut of levels of devices. Voice technology is not just pervasive in smart speakers and smart phones - it is finding its way into wearables, vehicles, homes, and even consumer and clinical medical devices. We even have smart jewelry emerging with health, wellness, and safety features built in. Best of all, this trend spans intergenerational health and wellness that goes beyond clinical care into long term health and wellbeing and the potential for increased patient engagement. In this book, the editors review information from the top thought-leaders in this space and examine real-world case studies of the outcomes and potential of voice technology in healthcare. Topics include a market survey, clinical use cases, home health use cases, health and wellness topics - fitness, nutrition, and wellbeing; next generation fitness facilities; voice and wearables in smart, connected communities; voice technology in social companions/robots; voice technology in the future surgical suites; a roadmap for the future from top technology; standards in voice technology; and the future of voice technology and artificial intelligence.

What will the future be? A dystopian landscape controlled by machines or a brave new world full of possibilities? Perhaps the answer lies with Artificial Intelligence (AI)-a phenomenon much beyond technology that has, continues to, and will shape lives in ways we do not understand yet. This book traces the evolution of AI in contemporary history. It analyses how AI is primarily being driven by "capital" as the only "factor of production" and its consequences for the global political economy. It further explores the dystopian prospect of mass unemployment by AI and takes up the ethical aspects of AI and its possible use in undermining natural and fundamental rights. A tract for the times, this volume will be a major intervention in an area that is heavily debated but rarely understood. It will be essential reading for researchers and students of digital humanities, politics, economics, science and technology studies, physics, and computer science. It will also be key reading for policy makers, cyber experts and bureaucrats.

DATA SCIENCE HANDBOOK This desk reference handbook gives a hands-on experience on various algorithms and popular techniques used in real-time in data science to all researchers working in various domains. Data Science is one of the leading research-driven areas in the modern era. It is having a critical role in healthcare, engineering, education, mechatronics, and medical robotics. Building models and working with data is not value-neutral. We choose the problems with which we work, make assumptions in these models, and decide on metrics and algorithms for the problems. The data scientist identifies the problem which can be solved with data and expert tools of modeling and coding. The book starts with introductory concepts in data science like data munging, data preparation, and transforming data. Chapter 2 discusses data visualization, drawing various plots and histograms. Chapter 3 covers mathematics and statistics for data science. Chapter 4 mainly focuses on machine learning algorithms in data science. Chapter 5 comprises of outlier analysis and DBSCAN algorithm. Chapter 6 focuses on clustering. Chapter 7 discusses network analysis. Chapter 8 mainly focuses on regression and naive-bayes classifier. Chapter 9 covers web-based data visualizations with Plotly. Chapter 10 discusses web scraping. The book concludes with a section discussing 19 projects on various subjects in data science. Audience The handbook will be used by graduate students up to research scholars in computer science and electrical engineering as well as industry professionals in a range of industries such as healthcare.

What will the future be? A dystopian landscape controlled by machines or a brave new world full of possibilities? Perhaps the answer lies with Artificial Intelligence (AI)-a phenomenon much beyond technology that has, continues to, and will shape lives in ways we do not understand yet. This book traces the evolution of AI in contemporary history. It analyses how AI is primarily being driven by "capital" as the only "factor of production" and its consequences for the global political economy. It further explores the dystopian prospect of mass unemployment by AI and takes up the ethical aspects of AI and its possible use in undermining natural and fundamental rights. A tract for the times, this volume will be a major intervention in an area that is heavily debated but rarely understood. It will be essential reading for researchers and students of digital humanities, politics, economics, science and technology studies, physics, and computer science. It will also be key reading for policy makers, cyber experts and bureaucrats.

The idea of artificial intelligence--job-killing robots, self-driving cars, and self-managing organizations--captures the imagination, evoking a combination of wonder and dread for those of us who will have to deal with the consequences. But what if it's not quite so complicated? The real job of artificial intelligence, argue these three eminent economists, is to lower the cost of prediction. And once you start talking about costs, you can use some well‐established economics to cut through the hype.

The constant challenge for all managers is to make decisions under uncertainty. And AI contributes by making knowing what's coming in the future cheaper and more certain. But decision making has another component: judgment, which is firmly in the realm of humans, not machines. Making prediction cheaper means that we can make more predictions more accurately and assess them with our better (human) judgment. Once managers can separate tasks into components of prediction and judgment, we can begin to understand how to optimize the interface between humans and machines.

More than just an account of AI's powerful capabilities, Prediction Machines shows managers how they can most effectively leverage AI, disrupting business as usual only where required, and provides businesses with a toolkit to navigate the coming wave of challenges and opportunities.

This book gathers the proceedings of the 2nd Latin American Congress on Automation and Robotics, held at Pontificia Universidad Javeriana de Cali, Colombia, on October 30th-November 1st, 2019. It presents papers from researchers, scientists, and engineers from academia and industry, and explores current exciting research applications and future challenges, mainly in Latin American countries. The book covers a wide range of research fields associated with automation and robotics encountered in engineering, scientific research, and practice, including: autonomous systems, multi-robot and multi-agent systems, industrial automation and robotics, process control, modeling and optimization, control theory, artificial intelligence, kinematic and dynamic analysis of robotic systems, computer vision, self-localization, mapping and navigation, instruments, sensing and sensor fusion, evolutionary, bio-inspired, micro/nano, and soft robotics, novel robot designs, haptics, human-robot interaction and interfaces, simulation procedures, experimental validations, and educational robotics.

A cohesive and insightful compilation of resources explaining the latest discoveries and methods in the field of nanoporous materials In Artificial Intelligence for Zeolites and Nanoporous Materials: Design, Synthesis and Properties Prediction a team of distinguished researchers delivers a robust compilation of the latest knowledge and most recent developments in computational chemistry, synthetic chemistry, and artificial intelligence as it applies to zeolites, porous molecular materials, covalent organic frameworks and metal-organic frameworks. The book presents a common language that unifies these fields of research and advances the discovery of new nanoporous materials. The editors have included resources that describe strategies to synthesize new nanoporous materials, construct databases of materials, structure directing agents, and synthesis conditions, and explain computational methods to generate new materials. They also offer material that discusses AI and machine learning algorithms, as well as other, similar approaches to the field. Readers will also find a comprehensive approach to artificial intelligence applied to and written in the language of materials chemistry, guiding the reader through the fundamental questions on how far computer algorithms and numerical representations can drive our search of new nanoporous materials for specific applications. Designed for academic researchers and industry professionals with an interest in synthetic nanoporous materials chemistry, Artificial Intelligence for Zeolites and Nanoporous Materials: Design, Synthesis and Properties Prediction will also earn a place in the libraries of professionals working in large energy, chemical, and biochemical companies with responsibilities related to the design of new nanoporous materials.

Epistemic logic has grown from its philosophical beginnings to find diverse applications in computer science as a means of reasoning about the knowledge and belief of agents. This book, based on courses taught at universities and summer schools, provides a broad introduction to the subject; many exercises are included together with their solutions. The authors begin by presenting the necessary apparatus from mathematics and logic, including Kripke semantics and the well-known modal logics K, T, S4 and S5. Then they turn to applications in the contexts of distributed systems and artificial intelligence: topics that are addressed include the notions of common knowledge, distributed knowledge, explicit and implicit belief, the interplays between knowledge and time, and knowledge and action, as well as a graded (or numerical) variant of the epistemic operators. The problem of logical omniscience is also discussed extensively. Halpern and Moses' theory of honest formulae is covered, and a digression is made into the realm of non-monotonic reasoning and preferential entailment. Moore's autoepistemic logic is discussed, together with Levesque's related logic of 'all I know'. Furthermore, it is shown how one can base default and counterfactual reasoning on epistemic logic.

Living with Robots recounts a foundational shift in the field of robotics, from artificial intelligence to artificial empathy, and foreshadows an inflection point in human evolution. Today's robots engage with human beings in socially meaningful ways, as therapists, trainers, mediators, caregivers, and companions. Social robotics is grounded in artificial intelligence, but the field's most probing questions explore the nature of the very real human emotions that social robots are designed to emulate. Social roboticists conduct their inquiries out of necessity-every robot they design incorporates and tests a number of hypotheses about human relationships. Paul Dumouchel and Luisa Damiano show that as roboticists become adept at programming artificial empathy into their creations, they are abandoning the conventional conception of human emotions as discrete, private, internal experiences. Rather, they are reconceiving emotions as a continuum between two actors who coordinate their affective behavior in real time. Rethinking the role of sociability in emotion has also led the field of social robotics to interrogate a number of human ethical assumptions, and to formulate a crucial political insight: there are simply no universal human characteristics for social robots to emulate. What we have instead is a plurality of actors, human and nonhuman, in noninterchangeable relationships. As Living with Robots shows, for social robots to be effective, they must be attentive to human uniqueness and exercise a degree of social autonomy. More than mere automatons, they must become social actors, capable of modifying the rules that govern their interplay with humans.

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students, and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the Eighth International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2019), which took place in Lisbon, Portugal, on December 10-12, 2019. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, and network dynamics; diffusion, epidemics, and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

This book examines real-world implementations of service automation technologies using Robotic Process Automation and Cognitive Automation tools. This newest, detailed research finds that RPA adoptions are accelerating, maturing, and scaling in global enterprise. The research covers multiple industries, applications, and shared services, and uses case studies to establish action principles and how to mitigate automation risks. The book also examines the first enterprise-worthy cognitive automation tools that use machine-learning algorithms to process big data, often in natural language form, and analyses three major detailed cases and the conditions for effective implementation. The book includes interviews with major clients, providers and analysts, and a detailed analysis of the automation and future of work debate. The book provides a compelling and incisive, evidence-based perspective on the direction and management of service automation, taking trends through to 2025. Automation technologies like RPA, CA, and the newest Blockchain technologies are found to transform and elevate human work rather than eliminate it.

Intelligent Autonomy of UAVs: Advanced Missions and Future Use provides an approach to the formulation of the fundamental task typical to any mission and provides guidelines of how this task can be solved by different generic robotic problems. As such, this book aims to provide a systems engineering approach to UAV projects, discovering the real problems that need to be resolved independently of the application. After an introduction to the rapidly evolving field of aerial robotics, the book presents topics such as autonomy, mission analysis, human-UAV teams, homogeneous and heterogeneous UAV teams, and finally, UAV-UGV teams. It then covers generic robotic problems such as orienteering and coverage. The book next introduces deployment, patrolling, and foraging, while the last part of the book tackles an important application: aerial search, tracking, and surveillance. This book is meant for both scientists and practitioners. For practitioners, it presents existing solutions that are categorized according to various missions: surveillance and reconnaissance, 3D mapping, urban monitoring, precision agriculture, forestry, disaster assessment and monitoring, security, industrial plant inspection, etc. For scientists, it provides an overview of generic robotic problems such as coverage and orienteering; deployment, patrolling and foraging; search, tracking, and surveillance. The design and analysis of algorithms raise a unique combination of questions from many fields, including robotics, operational research, control theory, and computer science.

The integration of robotic systems and artificial intelligence into healthcare settings is accelerating. As these technological developments interact socially with children, the elderly, or the disabled, they may raise concerns besides mere physical safety; concerns that include data protection, inappropriate use of emotions, invasion of privacy, autonomy suppression, decrease in human interaction, and cognitive safety. Given the novelty of these technologies and the uncertainties surrounding the impact of care automation, it is unclear how the law should respond. This book investigates the legal and regulatory implications of the growing use of personal care robots for healthcare purposes. It explores the interplay between various aspects of the law, including safety, data protection, responsibility, transparency, autonomy, and dignity; and it examines different robotic and AI systems, such as social therapy robots, physical assistant robots for rehabilitation, and wheeled passenger carriers. Highlighting specific problems and challenges in regulating complex cyber-physical systems in concrete healthcare applications, it critically assesses the adequacy of current industry standards and emerging regulatory initiatives for robots and AI. After analyzing the potential legal and ethical issues associated with personal care robots, it concludes that the primarily principle-based approach of recent law and robotics studies is too abstract to be as effective as required by the personal care context. Instead, it recommends bridging the gap between general legal principles and their applicability in concrete robotic and AI technologies with a risk-based approach using impact assessments. As the first book to compile both legal and regulatory aspects of personal care robots, this book will be a valuable addition to the literature on robotics, artificial intelligence, human-robot interaction, law, and philosophy of technology.

This book aims to answer two questions that are fundamental to the study of agent-based economic models: what is agent-based computational economics and why do we need agent-based economic modelling of economy? This book provides a review of the development of agent-based computational economics (ACE) from a perspective on how artificial economic agents are designed under the influences of complex sciences, experimental economics, artificial intelligence, evolutionary biology, psychology, anthropology and neuroscience. This book begins with a historical review of ACE by tracing its origins. From a modelling viewpoint, ACE brings truly decentralized procedures into market analysis, from a single market to the whole economy. This book also reviews how experimental economics and artificial intelligence have shaped the development of ACE. For the former, the book discusses how ACE models can be used to analyse the economic consequences of cognitive capacity, personality and cultural inheritance. For the latter, the book covers the various tools used to construct artificial adaptive agents, including reinforcement learning, fuzzy decision rules, neural networks, and evolutionary computation. This book will be of interest to graduate students researching computational economics, experimental economics, behavioural economics, and research methodology.

Soft Numerical Computing in Uncertain Dynamic Systems is intended for system specialists interested in dynamic systems that operate at different time scales. The book discusses several types of errors and their propagation, covering numerical methods-including convergence and consistence properties and characteristics-and proving of related theorems within the setting of soft computing. Several types of uncertainty representation like interval, fuzzy, type 2 fuzzy, granular, and combined uncertain sets are discussed in detail. The book can be used by engineering students in control and finite element fields, as well as all engineering, applied mathematics, economics, and computer science students. One of the important topics in applied science is dynamic systems and their applications. The authors develop these models and deliver solutions with the aid of numerical methods. Since they are inherently uncertain, soft computations are of high relevance here. This is the reason behind investigating soft numerical computing in dynamic systems. If these systems are involved with complex-uncertain data, they will be more practical and important. Real-life problems work with this type of data and most of them cannot be solved exactly and easily-sometimes they are impossible to solve. Clearly, all the numerical methods need to consider error of approximation. Other important applied topics involving uncertain dynamic systems include image processing and pattern recognition, which can benefit from uncertain dynamic systems as well. In fact, the main objective is to determine the coefficients of a matrix that acts as the frame in the image. One of the effective methods exhibiting high accuracy is to use finite differences to fill the cells of the matrix.

This book addresses the challenging topic of modeling adaptive networks, which often manifest inherently complex behavior. Networks by themselves can usually be modeled using a neat, declarative, and conceptually transparent Network-Oriented Modeling approach. In contrast, adaptive networks are networks that change their structure; for example, connections in Mental Networks usually change due to learning, while connections in Social Networks change due to various social dynamics. For adaptive networks, separate procedural specifications are often added for the adaptation process. Accordingly, modelers have to deal with a less transparent, hybrid specification, part of which is often more at a programming level than at a modeling level. This book presents an overall Network-Oriented Modeling approach that makes designing adaptive network models much easier, because the adaptation process, too, is modeled in a neat, declarative, and conceptually transparent Network-Oriented Modeling manner, like the network itself. Thanks to this approach, no procedural, algorithmic, or programming skills are needed to design complex adaptive network models. A dedicated software environment is available to run these adaptive network models from their high-level specifications. Moreover, because adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive (second-order adaptation), too can be modeled just as easily. For example, this can be applied to model metaplasticity in cognitive neuroscience, or second-order adaptation in biological and social contexts. The book illustrates the usefulness of this approach via numerous examples of complex (higher-order) adaptive network models for a wide variety of biological, mental, and social processes. The book is suitable for multidisciplinary Master's and Ph.D. students without assuming much prior knowledge, although also some elementary mathematical analysis is involved. Given the detailed information provided, it can be used as an introduction to Network-Oriented Modeling for adaptive networks. The material is ideally suited for teaching undergraduate and graduate students with multidisciplinary backgrounds or interests. Lecturers will find additional material such as slides, assignments, and software.

Studies on integer optimization in emergency management have attracted engineers and scientists from various disciplines such as management, mathematics, computer science, and other fields. Although there are a large number of literature reports on integer planning and emergency events, few books systematically explain the combination of the two. Researchers need a clear and thorough presentation of the theory and application of integer programming methods for emergency management. Integer Optimization and its Computation in Emergency Management investigates the computation theory of integer optimization, developing integer programming methods for emergency management and explores related practical applications. Pursuing a holistic approach, this book establishes a fundamental framework for this topic, intended for graduate students who are interested in operations research and optimization, researchers investigating emergency management, and algorithm design engineers working on integer programming or other optimization applications.

This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.